Relevance and kernel self-organising maps

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2714, Issue , 2003, Pages 280-287

  Corchado, Emilio ^a   Fyfe, Colin ^a  

^a UNIVERSITY OF THE WEST OF SCOTLAND   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTERS;

ARTIFICIAL DATA; FAST CONVERGENCE; INCREMENTAL LEARNING; IRIS DATA; OVER-COMPLETE; RELEVANCE VECTOR MACHINE; SELF-ORGANISING; SUPERVISED TRAININGS;

NEURAL NETWORKS;

EID: 35248892931     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-44989-2_34     Document Type: Article

References (6)

1. D. MacDonald and C. Fyfe. The kernel self-organising map. In R.J. Howlett and L. C. Jain, editors, Fourth International Conference on Knowledge-based Intelligent Engineering Systems and Allied Technologies, KES2000, 2000.
2. D. Michie, D. J. Spiegelhalter, and C. C. Taylor, editors. Machine learning, neural and statistical classification. Ellis Horwood, 1994.
3. B. Schölkopf. The kernel trick for distances. Technical report, Microsoft Research, May 2000.
4. B. Schölkopf, A. Smola, and K.-R. Muller. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10:1299-1319, 1998.
5. A. J. Smola, S. Mika, B. Schölkopf, and R. C. Williamson. Regularized principal maniforlds. Machine Learning, pages 1-28, 2000.
6. M. Tipping. The relevance vector machine. In S. A. Solla, T. K. Leen, and K.-R. Muller, editors, Advances in Neural Information Processing Systems, 12. MIT Press, 2000.